Question 1

Two series are said to be cointegrated if:

Accepted Answer

A) both series are I(1)but a linear combination of them is I(0). 
B) both series are I(0)but a linear combination of them is I(1). 
C) both series have the same set of explanatory variables but a different dependent variable. 
D) both series have the same dependent variable but a different set of explanatory variables. 
A) both series are I(1)but a linear combination of them is I(0). 
B) both series are I(0)but a linear combination of them is I(1). 
C) both series have the same set of explanatory variables but a different dependent variable. 
D) both series have the same dependent variable but a different set of explanatory variables. B

Question 2

Exponential smoothing is a forecasting method where the weights on the lagged dependent variable decline to zero exponentially.

Accepted Answer

A) True 
 B)False  True

Question 3

Let {(yt,zt): t = …,2,1,0,1,2,…} be a bivariate time series process.The model: yt= α + βozt + β1zt - 1 + β2zt - 2 + …..+ ut,where t = ….. ,-2,-1,0,1,2,……,represents a(n):

Accepted Answer

A) moving average model. 
B) ARIMA model. 
C) finite distributed lag model. 
D) infinite distributed lag model. 
A) moving average model. 
B) ARIMA model. 
C) finite distributed lag model. 
D) infinite distributed lag model. D

Question 4

Which of the following statements is true of spurious regressions?

Accepted Answer

A) The OLS estimates of the population parameters are efficient and unbiased and the t statistic is valid. 
B) Even if the explanatory variables and the dependent variable are independent times series processes,the R2 can large. 
C) Spurious regressions are limited to I(0)processes,and are not possible in case of I(1)processes. 
D) Spurious regressions are limited to I(1)processes,and are not possible in case of I(0)processes. 
A) The OLS estimates of the population parameters are efficient and unbiased and the t statistic is valid. 
B) Even if the explanatory variables and the dependent variable are independent times series processes,the R2 can large. 
C) Spurious regressions are limited to I(0)processes,and are not possible in case of I(1)processes. 
D) Spurious regressions are limited to I(1)processes,and are not possible in case of I(0)processes.

Question 5

Vector autoregressive models should be used for forecasting if the series being studied are cointegrated.

Accepted Answer

A) True 
 B)False

Question 6

Which of the following statements correctly identifies the difference between an autoregressive model and a vector autoregressive model?

Accepted Answer

A) In an autoregressive model,the dependent variable is expressed as a function of its own lag,whereas in a vector autoregressive model,the dependent variable is expressed as a function of the lag of an explanatory variable. 
B) In an autoregressive model,the dependent variable is expressed as a function of the lag of an explanatory variable,whereas in a vector autoregressive model,the dependent variable is expressed as a function of its own lag. 
C) In an autoregressive model several series are modelled in terms of their own past,whereas in a vector autoregressive model only one series is modelled in terms of its own past. 
D) In an autoregressive model one series is modelled in terms of its own past,whereas in a vector autoregressive model several series are modelled in terms of their past. 
A) In an autoregressive model,the dependent variable is expressed as a function of its own lag,whereas in a vector autoregressive model,the dependent variable is expressed as a function of the lag of an explanatory variable. 
B) In an autoregressive model,the dependent variable is expressed as a function of the lag of an explanatory variable,whereas in a vector autoregressive model,the dependent variable is expressed as a function of its own lag. 
C) In an autoregressive model several series are modelled in terms of their own past,whereas in a vector autoregressive model only one series is modelled in terms of its own past. 
D) In an autoregressive model one series is modelled in terms of its own past,whereas in a vector autoregressive model several series are modelled in terms of their past.

Question 7

Which of the following is true of squared forecast errors?

Accepted Answer

A) An error of +4 yields a greater loss than an error of -4. 
B) An error of -4 yields a greater loss than an error of +4. 
C) An error of -4 or +4 yields the same loss. 
D) Loss from positive and negative forecast errors cannot be compared. 
A) An error of +4 yields a greater loss than an error of -4. 
B) An error of -4 yields a greater loss than an error of +4. 
C) An error of -4 or +4 yields the same loss. 
D) Loss from positive and negative forecast errors cannot be compared.

Question 8

In case of forecasts,the root mean squared error is the:

Accepted Answer

A) average of the forecast errors divided by the variance of the errors. 
B) average of the absolute forecast errors. 
C) standard deviation of the forecast errors without any degrees of freedom adjustment. 
D) standard deviation of the forecast errors with a degrees of freedom adjustment. 
A) average of the forecast errors divided by the variance of the errors. 
B) average of the absolute forecast errors. 
C) standard deviation of the forecast errors without any degrees of freedom adjustment. 
D) standard deviation of the forecast errors with a degrees of freedom adjustment.

Question 9

A spurious correlation refers to a situation where:

Accepted Answer

A) two variables are related through their correlation with a third variable. 
B) the correlation coefficient between two variables cannot be estimated. 
C) there is direct causal relationship between two variables but tests for correlations reject this relationship. 
D) the correlation between two variables is positive till the sample size reaches a threshold,and negative after the sample size crosses the threshold. 
A) two variables are related through their correlation with a third variable. 
B) the correlation coefficient between two variables cannot be estimated. 
C) there is direct causal relationship between two variables but tests for correlations reject this relationship. 
D) the correlation between two variables is positive till the sample size reaches a threshold,and negative after the sample size crosses the threshold.

Question 10

A spurious regression refers to a situation where:

Accepted Answer

A) the direction of the relationship between the dependent variable and the explanatory variables is uncertain. 
B) even though two variables are independent,the OLS regression of one variable on the other indicates a relationship between them. 
C) a few important and necessary explanatory variables are left out of a regression equation,thus leading to inefficient and inconsistent forecasts. 
D) at least one of the variables used in a regression equation does not have a unit root and the error terms are heteroskedastic. 
A) the direction of the relationship between the dependent variable and the explanatory variables is uncertain. 
B) even though two variables are independent,the OLS regression of one variable on the other indicates a relationship between them. 
C) a few important and necessary explanatory variables are left out of a regression equation,thus leading to inefficient and inconsistent forecasts. 
D) at least one of the variables used in a regression equation does not have a unit root and the error terms are heteroskedastic.

Question 11

Which of the following is used to test whether a time series follows a unit root process?

Accepted Answer

A) Wald test 
B) White test 
C) Augmented Dickey-Fuller test 
D) Johansen test 
A) Wald test 
B) White test 
C) Augmented Dickey-Fuller test 
D) Johansen test

Question 12

If the t statistic for the presence of a unit root in a variable is -7.22 and the 5% critical value is -2.86,there is strong evidence against a unit root in the variable.

Accepted Answer

A) True 
 B)False

Question 13

The model: yt? = α0 + γ0?zt +ρyt - 1 + γ1?zt - 1 +vt,where vt= ut - ρut - 1 ?represents a:

Accepted Answer

A) finite distributed lag model. 
B) simultaneous equations model. 
C) rational distributed lag model. 
D) vector error correction model. 
A) finite distributed lag model. 
B) simultaneous equations model. 
C) rational distributed lag model. 
D) vector error correction model.

Question 14

The Koyck distributed lag model is an example of:

Accepted Answer

A) a moving average model. 
B) an autoregressive conditional heteroskedasticity model. 
C) an infinite distributed lag model. 
D) a finite distributed lag model. 
A) a moving average model. 
B) an autoregressive conditional heteroskedasticity model. 
C) an infinite distributed lag model. 
D) a finite distributed lag model.

Question 15

If ft denotes the forecast of yt+1made at time t,then the forecast error is given by:

Accepted Answer

A) et+1 = ft/yt+1. 
B) et+1 =yt+1/ft?. 
C) et+1 = yt+1+ ft?. 
D) et+1= yt+1- ft. 
A) et+1 = ft/yt+1. 
B) et+1 =yt+1/ft?. 
C) et+1 = yt+1+ ft?. 
D) et+1= yt+1- ft.

Question 16

Which of the following tests can be used to check for cointegration between two series?

Accepted Answer

A) Wald test 
B) Breush-Pagan test 
C) White test 
D) Engle-Granger test 
A) Wald test 
B) Breush-Pagan test 
C) White test 
D) Engle-Granger test

Question 17

In calculation of squared forecast errors,an error of +3 yields a loss three times greater than an error of -1.

Accepted Answer

A) True 
 B)False

Question 18

The R2calculated in a spurious regression is a valid and efficient estimate of the goodness-of-fit of the regression equation.

Accepted Answer

A) True 
 B)False

Question 19

In the given AR(1)model,yt = α + ρyt - 1,t = 1,2…… ,the Dickey-Fuller distribution refers to the:

Accepted Answer

A) asymptotic distribution of the t statistic under the hypothesis ρ - 1 = 0. 
B) asymptotic distribution of the F statistic under the hypothesis ρ - 1 = 0. 
C) asymptotic distribution of the χ2 statistic under the hypothesis ρ - 1 = 0. 
D) asymptotic distribution of the z statistic under the hypothesis ρ - 1 = 0. 
A) asymptotic distribution of the t statistic under the hypothesis ρ - 1 = 0. 
B) asymptotic distribution of the F statistic under the hypothesis ρ - 1 = 0. 
C) asymptotic distribution of the χ2 statistic under the hypothesis ρ - 1 = 0. 
D) asymptotic distribution of the z statistic under the hypothesis ρ - 1 = 0.

Question 20

Which of the following statements is true?

Accepted Answer

A) The calculated t statistic is valid and efficient in case of a spurious regression. 
B) If an explanatory variable or a dependent variable is integrated of the order one,the OLS estimators are asymptotically normally distributed. 
C) An error correction model can be used to study the short-run dynamics in the relationship between the dependent variable and the explanatory variables in a time series model. 
D) The Dickey-Fuller test can be used to test for heteroskedasticity in the error terms. 
A) The calculated t statistic is valid and efficient in case of a spurious regression. 
B) If an explanatory variable or a dependent variable is integrated of the order one,the OLS estimators are asymptotically normally distributed. 
C) An error correction model can be used to study the short-run dynamics in the relationship between the dependent variable and the explanatory variables in a time series model. 
D) The Dickey-Fuller test can be used to test for heteroskedasticity in the error terms.

Two series are said to be cointegrated if:

Exponential smoothing is a forecasting method where the weights on the lagged dependent variable decline to zero exponentially.

Let {(yt,zt): t = …,2,1,0,1,2,…} be a bivariate time series process.The model: y_t= α + β_oz_t + β₁z_{t - 1}+ β₂z_{t - 2}+ …..+ u_t,where t = ….. ,-2,-1,0,1,2,……,represents a(n):

Which of the following statements is true of spurious regressions?

Vector autoregressive models should be used for forecasting if the series being studied are cointegrated.

Which of the following statements correctly identifies the difference between an autoregressive model and a vector autoregressive model?

Which of the following is true of squared forecast errors?

In case of forecasts,the root mean squared error is the:

A spurious correlation refers to a situation where:

A spurious regression refers to a situation where:

Which of the following is used to test whether a time series follows a unit root process?

If the t statistic for the presence of a unit root in a variable is -7.22 and the 5% critical value is -2.86,there is strong evidence against a unit root in the variable.

The model: y_t_? = α₀ + γ₀_?z_t+ρy_{t - 1}+ γ₁_?z_{t - 1}+v_t,where v_t= u_t - ρu_{t - 1}?represents a:

The Koyck distributed lag model is an example of:

If f_t denotes the forecast of y_t+1made at time t,then the forecast error is given by:

Which of the following tests can be used to check for cointegration between two series?

In calculation of squared forecast errors,an error of +3 yields a loss three times greater than an error of -1.

The R²calculated in a spurious regression is a valid and efficient estimate of the goodness-of-fit of the regression equation.

In the given AR(1)model,y_t = α + ρy_{t - 1},t = 1,2…… ,the Dickey-Fuller distribution refers to the:

Which of the following statements is true?

The Nature of Econometrics and Economic Data

The Simple Regression Model

Multiple Regression Analysis: Estimation

Multiple Regression Analysis: Inference

Multiple Regression Analysis: Ols Asymptotics

Multiple Regression Analysis: Further Issues

Multiple Regression Analysis With Qualitative Information: Binary or Dummy Variables

Heteroskedasticity

More on Specification and Data Problems

Basic Regression Analysis With Time Series Data

Further Issues in Using Ols With Time Series Data

Serial Correlation and Heteroskedasticity in Time Series Regressions

Pooling Cross Sections Across Time: Simple Panel Data Methods

Advanced Panel Data Methods

Instrumental Variables Estimation and Two Stage Least Squares

Simultaneous Equations Models

Limited Dependent Variable Models and Sample Selection Corrections

Carrying Out an Empirical Project

Filters

Exam 18: Advanced Time Series Topics

Two series are said to be cointegrated if:

Exponential smoothing is a forecasting method where the weights on the lagged dependent variable decline to zero exponentially.

Let {(yt,zt): t = …,2,1,0,1,2,…} be a bivariate time series process.The model: yt= α + βozt + β1zt - 1 + β2zt - 2 + …..+ ut,where t = ….. ,-2,-1,0,1,2,……,represents a(n):

Which of the following statements is true of spurious regressions?

Vector autoregressive models should be used for forecasting if the series being studied are cointegrated.

Which of the following statements correctly identifies the difference between an autoregressive model and a vector autoregressive model?

Which of the following is true of squared forecast errors?

In case of forecasts,the root mean squared error is the:

A spurious correlation refers to a situation where:

A spurious regression refers to a situation where:

Which of the following is used to test whether a time series follows a unit root process?

If the t statistic for the presence of a unit root in a variable is -7.22 and the 5% critical value is -2.86,there is strong evidence against a unit root in the variable.

The model: yt? = α0 + γ0?zt +ρyt - 1 + γ1?zt - 1 +vt,where vt= ut - ρut - 1 ?represents a:

The Koyck distributed lag model is an example of:

If ft denotes the forecast of yt+1made at time t,then the forecast error is given by:

Which of the following tests can be used to check for cointegration between two series?

In calculation of squared forecast errors,an error of +3 yields a loss three times greater than an error of -1.

The R2calculated in a spurious regression is a valid and efficient estimate of the goodness-of-fit of the regression equation.

In the given AR(1)model,yt = α + ρyt - 1,t = 1,2…… ,the Dickey-Fuller distribution refers to the:

Which of the following statements is true?

The Nature of Econometrics and Economic Data

The Simple Regression Model

Multiple Regression Analysis: Estimation

Multiple Regression Analysis: Inference

Multiple Regression Analysis: Ols Asymptotics

Multiple Regression Analysis: Further Issues

Multiple Regression Analysis With Qualitative Information: Binary or Dummy Variables

Heteroskedasticity

More on Specification and Data Problems

Basic Regression Analysis With Time Series Data

Further Issues in Using Ols With Time Series Data

Serial Correlation and Heteroskedasticity in Time Series Regressions

Pooling Cross Sections Across Time: Simple Panel Data Methods

Advanced Panel Data Methods

Instrumental Variables Estimation and Two Stage Least Squares

Simultaneous Equations Models

Limited Dependent Variable Models and Sample Selection Corrections

Carrying Out an Empirical Project

Filters

Let {(yt,zt): t = …,2,1,0,1,2,…} be a bivariate time series process.The model: y_t= α + β_oz_t + β₁z_{t - 1}+ β₂z_{t - 2}+ …..+ u_t,where t = ….. ,-2,-1,0,1,2,……,represents a(n):

The model: y_t_? = α₀ + γ₀_?z_t+ρy_{t - 1}+ γ₁_?z_{t - 1}+v_t,where v_t= u_t - ρu_{t - 1}?represents a:

If f_t denotes the forecast of y_t+1made at time t,then the forecast error is given by:

The R²calculated in a spurious regression is a valid and efficient estimate of the goodness-of-fit of the regression equation.

In the given AR(1)model,y_t = α + ρy_{t - 1},t = 1,2…… ,the Dickey-Fuller distribution refers to the: